diff --git a/vessel_detection.py b/vessel_detection.py
new file mode 100644
index 0000000..a9c2760
--- /dev/null
+++ b/vessel_detection.py
@@ -0,0 +1,271 @@
+from __future__ import print_function
+
+from multiprocessing.pool import ThreadPool
+
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import pylab as pl
+from PyQt5.QtWidgets import QFileDialog
+
+def showImg(imgName, img, wsize=(400, 400)):
+    cv2.namedWindow(imgName, cv2.WINDOW_NORMAL)
+    cv2.resizeWindow(imgName, wsize[0], wsize[1])
+    cv2.imshow(imgName, img)
+
+def homofilter(I):
+    I = np.double(I)
+    m, n = I.shape
+    rL = 0.5
+    rH = 2
+    c = 2
+    d0 = 20
+    I1 = np.log(I + 1)
+    FI = np.fft.fft2(I1)
+    n1 = np.floor(m / 2)
+    n2 = np.floor(n / 2)
+    D = np.zeros((m, n))
+    H = np.zeros((m, n))
+    for i in range(m):
+        for j in range(n):
+            D[i, j] = ((i - n1) ** 2 + (j - n2) ** 2)
+            H[i, j] = (rH - rL) * (np.exp(c * (-D[i, j] / (d0 ** 2)))) + rL
+    I2 = np.fft.ifft2(H * FI)
+    I3 = np.real(np.exp(I2) - 1)
+    I4 = I3 - np.min(I3)
+    I4 = I4 / np.max(I4) * 255
+    dstImg = np.uint8(I4)
+    return dstImg
+
+def gaborfilter(srcImg):
+    dstImg = np.zeros(srcImg.shape[0:2])
+    filters = []
+    ksize = [5, 7, 9, 11, 13]
+    j = 0
+    for K in range(len(ksize)):
+        for i in range(12):
+            theta = i * np.pi / 12 + np.pi / 24
+            gaborkernel = cv2.getGaborKernel((ksize[K], ksize[K]), sigma=2 * np.pi, theta=theta, lambd=np.pi / 2,
+                                             gamma=0.5)
+            gaborkernel /= 1.5 * gaborkernel.sum()
+            filters.append(gaborkernel)
+    for kernel in filters:
+        gaborImg = cv2.filter2D(srcImg, cv2.CV_8U, kernel)
+        np.maximum(dstImg, gaborImg, dstImg)
+    return np.uint8(dstImg)
+
+def process(img, filters):
+    accum = np.zeros_like(img)
+    for kern in filters:
+        fimg = cv2.filter2D(img, cv2.CV_8U, kern, borderType=cv2.BORDER_REPLICATE)
+        np.maximum(accum, fimg, accum)
+    return accum
+
+def process_threaded(img, filters, threadn=8):
+    accum = np.zeros_like(img)
+
+    def f(kern):
+        return cv2.filter2D(img, cv2.CV_8U, kern)
+
+    pool = ThreadPool(processes=threadn)
+    for fimg in pool.imap_unordered(f, filters):
+        np.maximum(accum, fimg, accum)
+    return accum
+
+###    Gabor特征提取
+def getGabor(img, filters):
+    res = []  # 滤波结果
+    for i in range(len(filters)):
+        res1 = process(img, filters[i])
+        res.append(np.asarray(res1))
+
+    pl.figure(2)
+    for temp in range(len(res)):
+        pl.subplot(4, 6, temp + 1)
+        pl.imshow(res[temp], cmap='gray')
+    pl.show()
+    return res  # 返回滤波结果,结果为24幅图，按照gabor角度排列
+
+def build_filters():
+    filters = []
+    ksize = 31
+    for theta in np.arange(0, np.pi, np.pi / 16):
+        # kern = cv2.getGaborKernel((ksize, ksize), 4.0, theta, 10.0, 0.5, 0, ktype=cv2.CV_32F)
+        kern = cv2.getGaborKernel((ksize, ksize), 2 * np.pi, theta, 17.0, 0.5, 0, ktype=cv2.CV_32F)
+        kern /= 1.5 * kern.sum()
+        filters.append(kern)
+    return filters
+
+def print_gabor(filters):
+    for i in range(len(filters)):
+        showImg(str(i), filters[i])
+
+def reverse_image(img):
+    antiImg = np.zeros_like(img, dtype=np.uint8)
+    for i in range(img.shape[0]):
+        for j in range(img.shape[1]):
+            antiImg[i][j] = 255 - img[i][j]
+    return antiImg
+
+def pass_mask(mask, img):
+    # qwe = reverse_image(img)
+    qwe = img.copy()
+    for i in range(mask.shape[0]):
+        for j in range(mask.shape[1]):
+            if mask[i][j] == 0:
+                qwe[i][j] = 0
+    # asd = cv2.filter2D(qwe, cv2.CV_8U, mask)
+    return qwe
+
+def showKern(filters):
+    for i in list(range(16)):
+        kern = filters[i]
+        kern = kern - np.min(kern)
+        kern = kern / np.max(kern) * 255
+        kern = np.clip(kern, 0, 255)
+        kern = np.uint8(kern)
+        plt.suptitle('Gabor matched filter kernel')
+        plt.subplot(4,4,i+1), plt.imshow(kern, 'gray'), plt.axis('off'), plt.title('theta=' + str(i) + r'/pi')
+    plt.show()
+
+def calcDice(predict_img, groundtruth_img):
+    predict = predict_img.copy()
+    groundtruth = groundtruth_img.copy()
+    predict[predict < 128] = 0
+    predict[predict >= 128] = 1
+    groundtruth[groundtruth < 128] = 0
+    groundtruth[groundtruth >= 128] = 1
+    predict_n = 1 - predict
+    groundtruth_n = 1 - groundtruth
+    TP = np.sum(predict * groundtruth)
+    FP = np.sum(predict * groundtruth_n)
+    TN = np.sum(predict_n * groundtruth_n)
+    FN = np.sum(predict_n * groundtruth)
+    # print(TP, FP, TN, FN)
+    dice = 2 * np.sum(predict * groundtruth) / (np.sum(predict) + np.sum(groundtruth))
+    return dice
+
+def adjust_gamma(imgs, gamma=1.0):
+    # assert (len(imgs.shape)==4)  #4D arrays
+    # assert (imgs.shape[1]==1)  #check the channel is 1
+    # build a lookup table mapping the pixel values [0, 255] to
+    # their adjusted gamma values
+    invGamma = 1.0 / gamma
+    table = np.array([((i / 255.0) ** invGamma) * 255 for i in np.arange(0, 256)]).astype("uint8")
+    # apply gamma correction using the lookup table
+    new_imgs = np.zeros_like(imgs)
+    for i in range(imgs.shape[0]):
+        for j in range(imgs.shape[1]):
+            new_imgs[i, j] = cv2.LUT(np.array(imgs[i, j], dtype=np.uint8), table)
+    return new_imgs
+
+def build_filters2(sigma=1, YLength=10):
+    filters = []
+    widthOfTheKernel = np.ceil(np.sqrt((6 * np.ceil(sigma) + 1) ** 2 + YLength ** 2))
+    if np.mod(widthOfTheKernel, 2) == 0:
+        widthOfTheKernel = widthOfTheKernel + 1
+    widthOfTheKernel = int(widthOfTheKernel)
+    # print(widthOfTheKernel)
+    for theta in np.arange(0, np.pi, np.pi / 16):
+        # theta = np.pi/4
+        matchFilterKernel = np.zeros((widthOfTheKernel, widthOfTheKernel), dtype=np.float64)
+        for x in range(widthOfTheKernel):
+            for y in range(widthOfTheKernel):
+                halfLength = (widthOfTheKernel - 1) / 2
+                x_ = (x - halfLength) * np.cos(theta) + (y - halfLength) * np.sin(theta)
+                y_ = -(x - halfLength) * np.sin(theta) + (y - halfLength) * np.cos(theta)
+                if abs(x_) > 3 * np.ceil(sigma):
+                    matchFilterKernel[x][y] = 0
+                elif abs(y_) > (YLength - 1) / 2:
+                    matchFilterKernel[x][y] = 0
+                else:
+                    matchFilterKernel[x][y] = -np.exp(-.5 * (x_ / sigma) ** 2) / (np.sqrt(2 * np.pi) * sigma)
+        m = 0.0
+        for i in range(matchFilterKernel.shape[0]):
+            for j in range(matchFilterKernel.shape[1]):
+                if matchFilterKernel[i][j] < 0:
+                    m = m + 1
+        mean = np.sum(matchFilterKernel) / m
+        for i in range(matchFilterKernel.shape[0]):
+            for j in range(matchFilterKernel.shape[1]):
+                if matchFilterKernel[i][j] < 0:
+                    matchFilterKernel[i][j] = matchFilterKernel[i][j] - mean
+        filters.append(matchFilterKernel)
+
+    return filters
+
+def Z_ScoreNormalization(x, mu, sigma):
+    x = (x - mu) / sigma
+    return x
+
+def sigmoid(X):
+    return 1.0 / (1 + np.exp(-float(X)))
+
+def Normalize(data):
+    k = np.zeros(data.shape, np.float64)
+    # k = np.zeros_like(data)
+    # m = np.average(data)
+    mx = np.max(data)
+    mn = np.min(data)
+    for i in range(data.shape[0]):
+        for j in range(data.shape[1]):
+            k[i][j] = (float(data[i][j]) - mn) / (mx - mn) * 255
+    qwe = np.array(k, np.uint8)
+    return qwe
+
+def grayStretch(img, m=60.0/255, e=8.0):
+    k = np.zeros(img.shape, np.float64)
+    ans = np.zeros(img.shape, np.float64)
+    mx = np.max(img)
+    mn = np.min(img)
+    for i in range(img.shape[0]):
+        for j in range(img.shape[1]):
+            k[i][j] = (float(img[i][j]) - mn) / (mx - mn)
+    eps = 0.01
+    for i in range(img.shape[0]):
+        for j in range(img.shape[1]):
+            ans[i][j] = 1 / (1 + (m / (k[i][j] + eps)) ** e) * 255
+    ans = np.array(ans, np.uint8)
+    return ans
+
+def vessel_division(image):
+        # 原图
+        srcImg = image.copy()
+
+        # 标定图
+        grayImg = cv2.split(srcImg)[1]
+
+        # 提取掩膜
+        ret0, th0 = cv2.threshold(grayImg, 30, 255, cv2.THRESH_BINARY)
+        mask = cv2.erode(th0, np.ones((7, 7), np.uint8))
+        # showImg("mask", mask)
+
+        # 高斯滤波
+        blurImg = cv2.GaussianBlur(grayImg, (5, 5), 0)
+        # cv2.imwrite("blurImg.png", blurImg)
+
+        # HE
+        heImg = cv2.equalizeHist(blurImg)
+        # cv2.imwrite("heImg.png", heImg)
+
+        # CLAHE 光均衡化+对比度增强
+        clahe = cv2.createCLAHE(clipLimit=2, tileGridSize=(10, 10))
+        claheImg = clahe.apply(blurImg)
+        # cv2.imwrite("claheImg.png", claheImg)
+
+        # 同态滤波 光均衡化
+        homoImg = homofilter(blurImg)
+
+        preMFImg = adjust_gamma(claheImg, gamma=1.5)
+        filters = build_filters2()
+        # showKern(filters)
+        gaussMFImg = process(preMFImg, filters)
+        gaussMFImg_mask = pass_mask(mask, gaussMFImg)
+        grayStretchImg = grayStretch(gaussMFImg_mask, m=30.0 / 255, e=8)
+
+        # 二值化
+        ret1, th1 = cv2.threshold(grayStretchImg, 30, 255, cv2.THRESH_OTSU)
+        predictImg = th1.copy()
+
+        return predictImg
+        
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